Statistics/Transcript
Transcript Title text reads, The Mysteries of Life with Tim and Moby. Tim and Moby are at the supermarket, looking at different brands of popcorn. TIM: Hmm, which popcorn is worthy to be served at our screening of Troll 2? Moby grabs a box of popcorn from the shelf called Kuh-razy Korn. The box reads, 99.9 percent kernel poppage… every batch! Moby beeps. TIM: Yeah, doubtful. On-screen, a letter appears. Text reads as Tim narrates: Dear Tim and Moby, What exactly is a statistic? From, Bruce TIM: A statistic is a number, fraction, or percentage that describes a piece of information. A label appears, reading, statistic. Moby holds up the popcorn with 99.9 percent on the box. TIM: This is a statistic; it describes the percentage of kernels that will pop in each bag of Kuh-razy Korn popcorn. Sometimes, you’ll see stats in graph form, like here. On-screen, Tim picks up a box of Puffy Pop popcorn. There is a bar chart on the back of the box. TIM: This bar chart describes how much bigger an average piece of Puffy Pop popcorn is than its competitors’. On-screen, the bar chart compares the size of popcorn pieces among three brands: Puffy Pop, Corn Meister, and Green Valley. The bar for Puffy Pop is taller than the other two. TIM: And sometimes, you’ll see stats represented with quantity words, like “less,” “more,” and “average.” On-screen, a box of Old Has Been Popcorn appears. It reads, 10 percent less fat, twice as much flavor. Moby beeps. TIM: No, they’re not just on popcorn boxes; stats are used to sell tons of different products. On-screen, products with statistics on the packaging appear. A package of bacon reads, now 50 percent porkier; a bottle of pills reads, pain relief in 20 minutes, guaranteed; a car advertisement reads, most reliable car in its class. TIM: But advertising is just one place you’ll see statistics. They’re also used in opinion polls, weather forecasts, professional sports, economics, and scientific and medical studies, just to name a few areas. On-screen, examples of statistics appear: an online opinion poll, a weather forecast, an unemployment report, and a newspaper. Moby beeps. TIM: Actually, they just make the numbers up. The end! Moby looks skeptically at Tim. TIM: Kidding! Statistics are generated through a process of gathering information, analyzing it, and interpreting it through numbers. On-screen, a scientist pulls a lever. Boxes labeled, information, and data, move down a conveyor belt. They pass through a machine and come out the other side as numbers, percent signs, and graphs. TIM: This whole process has its own field of math, called, uh, statistics! Moby beeps. TIM: Yeah, it’s not as easy as pulling a lever. The first step is identifying the population you want to analyze. A label appears, reading, population. Outlines of human figures fill the screen. TIM: A population can really be anything. For a medical researcher, a population might consist of all Americans with high blood pressure. On-screen, a U.S. map appears behind the human figures. TIM: For an astronomer, it could be all visible galaxies; On-screen, many galaxies appear. TIM: For a popcorn marketer, it could be the different brands of microwave popcorn. On-screen, popcorn appears. TIM: Of course, you can’t test every single person with high blood pressure or look at every galaxy in existence. So usually, you have to choose a sample: a randomly selected group within the population. A label appears, reading, sample. The number of humans on the US map decreases. The number of galaxies also decreases. TIM: Once that’s done, you gather data from the sample. The larger the sample, the better your data will represent the population. Moby beeps. TIM: Well, it depends on what you’re trying to find out. Statistical research often just involves observation. An astronomer might compare the traits of different galaxies, and a popcorn marketer might compare the sizes of different types of popcorn. A label appears, reading, observation. An astronomer jots down notes as she looks at galaxies on a screen. A ruler measures a piece of popcorn. TIM: In these kinds of studies, you simply observe the sample and record the data. Other statistical studies try to establish causality, or cause and effect. Like, a medical researcher might want to know if a certain drug lowers blood pressure. A label appears, reading, causality. A man gets his blood pressure measured. A bottle of prescription pills appears. TIM: So, he’ll perform a scientific experiment, comparing how different groups react to different amounts of the drug. On-screen, several people get their blood pressure measured. Moby beeps. TIM: Well, the next step is analyzing the data and turning it into statistics! Sometimes, the analysis is descriptive: It just uses numbers to interpret what the data mean. A label appears, reading, descriptive. An astronomer holds up a bar chart titled, Brightness of Different Galaxies. TIM: Some basic descriptive statistics include mean, or average. On-screen, a list of numbers appears. The list reads, 64, 77, 79, 80, 84, 86, 90, 90, 94, 96, 97, 98. An equation reads, 1,035 divided by 12 equals 86.25. 86.25 is circled. A label reads, mean. TIM: Median, or midpoint. On-screen, 86 and 90 are highlighted in the list of numbers. An equation reads, 86 plus 90 equals 176. Another equation reads, 176 divided by 2 equals 88. The 88 is circled. A label reads, median. TIM: And mode, or most common result. On-screen, the two 90s that appear in the list are highlighted. The number 90 appears in the center of the screen and is circled. A label reads, mode. TIM: But in many cases, statistical analysis looks for patterns in the data in order to make predictions. For example, if a blood pressure drug makes one out of four patients dizzy, that stat could be used to predict its effect on future patients. On-screen, a box of blood pressure pills appears. Text on the box reads, side effects: 25 percent of patients may experience dizziness. Moby points to a claim on a popcorn box and beeps. TIM: Yeah. Unfortunately, you can’t always trust the stats you see on products, or even in the news. For one thing, two analyses of the same data could potentially come up with two totally different conclusions. On-screen, two people stand side by side. One holds a blue pie chart with a red slice. The other holds up a red pie chart with a blue slice. TIM: And in some cases, misleading statistics are even used purposely to support a certain point of view. Moby beeps. TIM: Oh, there are lots of different ways to make stats say whatever you want. Manipulating, or messing with, the sample is one common method. Take this graph: It compares Puffy Pop against two other brands of popcorn. On-screen, the popcorn bar graph reappears. The bar for Puffy Pop is taller than the other two bars. TIM: That’s way too small a sample to be meaningful; there are dozens of brands of popcorn! On-screen, the bar graph expands to include two additional brands. Both have bars that are taller than the bar for Puffy Pop. TIM: On top of that, Puffy Pop may have chosen these two brands because they knew their popcorn was made up of smaller pieces. That’s called a sampling bias, when you select a non-random sample of the population. A label appears, reading, sampling bias. Moby beeps. TIM: No, I’m not calling Puffy Pop a liar; even if there is a sampling bias here, it could have been unintentional. But the point is, until you know where a stat comes from and how it was derived, you can’t be 100 percent sure it’s trustworthy. Moby beeps. He and Tim turn back to the various popcorns on the supermarket shelf. TIM: All right … so that leaves us back where we started. How are we going to know which of these brands is the best? Moby points his finger at the popcorn boxes, and shoots a microwave beam. Every box of popcorn on the shelf starts to pop. The entire store fills with popcorn, which spills out onto the street. TIM: Oh, no. Category:BrainPOP Transcripts Category:BrainPOP Math Transcripts